Overview

Dataset statistics

Number of variables8
Number of observations768
Missing cells1758
Missing cells (%)28.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.0 KiB
Average record size in memory96.0 B

Variable types

Numeric6
Unsupported2

Alerts

hdl_cholesterol_mmol_L_consolidated is highly overall correlated with total_cholesterol_mmol_LHigh correlation
ldl_cholesterol_mmol_L_consolidated is highly overall correlated with total_cholesterol_mmol_LHigh correlation
total_cholesterol_mmol_L is highly overall correlated with hdl_cholesterol_mmol_L_consolidated and 1 other fieldsHigh correlation
Age (at enrolment) has 14 (1.8%) missing valuesMissing
Sex has 768 (100.0%) missing valuesMissing
study_week has 768 (100.0%) missing valuesMissing
total_cholesterol_mmol_L has 59 (7.7%) missing valuesMissing
hdl_cholesterol_mmol_L_consolidated has 58 (7.6%) missing valuesMissing
ldl_cholesterol_mmol_L_consolidated has 58 (7.6%) missing valuesMissing
fasting_glucose_mmol_L has 32 (4.2%) missing valuesMissing
Sex is an unsupported type, check if it needs cleaning or further analysisUnsupported
study_week is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-11-25 06:48:37.976976
Analysis finished2025-11-25 06:48:39.616998
Duration1.64 second
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Age (at enrolment)
Real number (ℝ)

Missing 

Distinct183
Distinct (%)24.3%
Missing14
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.533554
Minimum18.1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T08:48:39.641932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile22
Q127.85
median33.95
Q339
95-th percentile46
Maximum51
Range32.9
Interquartile range (IQR)11.15

Descriptive statistics

Standard deviation7.3527855
Coefficient of variation (CV)0.21926651
Kurtosis-0.80768914
Mean33.533554
Median Absolute Deviation (MAD)5.95
Skewness0.055500259
Sum25284.3
Variance54.063454
MonotonicityNot monotonic
2025-11-25T08:48:39.692209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4032
 
4.2%
3431
 
4.0%
3931
 
4.0%
3729
 
3.8%
3027
 
3.5%
3125
 
3.3%
3823
 
3.0%
4122
 
2.9%
2621
 
2.7%
3521
 
2.7%
Other values (173)492
64.1%
ValueCountFrequency (%)
18.11
 
0.1%
18.81
 
0.1%
193
 
0.4%
19.31
 
0.1%
19.42
 
0.3%
19.51
 
0.1%
19.61
 
0.1%
209
1.2%
20.11
 
0.1%
20.61
 
0.1%
ValueCountFrequency (%)
511
 
0.1%
503
 
0.4%
49.11
 
0.1%
495
0.7%
488
1.0%
47.91
 
0.1%
47.21
 
0.1%
4710
1.3%
46.61
 
0.1%
46.41
 
0.1%

Sex
Unsupported

Missing  Rejected  Unsupported 

Missing768
Missing (%)100.0%
Memory size12.0 KiB

BMI (kg/m²)
Real number (ℝ)

Distinct248
Distinct (%)32.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.852803
Minimum15.1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T08:48:39.740661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile19.13
Q123
median26.7
Q331.5
95-th percentile40.54
Maximum57
Range41.9
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.6900116
Coefficient of variation (CV)0.24019168
Kurtosis1.6551874
Mean27.852803
Median Absolute Deviation (MAD)4.1
Skewness1.0682353
Sum21363.1
Variance44.756255
MonotonicityNot monotonic
2025-11-25T08:48:39.784378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.311
 
1.4%
2510
 
1.3%
21.810
 
1.3%
26.79
 
1.2%
27.48
 
1.0%
25.88
 
1.0%
32.38
 
1.0%
21.58
 
1.0%
22.98
 
1.0%
28.97
 
0.9%
Other values (238)680
88.5%
ValueCountFrequency (%)
15.11
0.1%
15.31
0.1%
161
0.1%
16.11
0.1%
16.61
0.1%
16.81
0.1%
16.91
0.1%
17.11
0.1%
17.21
0.1%
17.31
0.1%
ValueCountFrequency (%)
571
 
0.1%
56.11
 
0.1%
54.91
 
0.1%
54.31
 
0.1%
50.71
 
0.1%
50.41
 
0.1%
50.11
 
0.1%
49.83
0.4%
491
 
0.1%
46.42
0.3%

study_week
Unsupported

Missing  Rejected  Unsupported 

Missing768
Missing (%)100.0%
Memory size12.0 KiB

total_cholesterol_mmol_L
Real number (ℝ)

High correlation  Missing 

Distinct331
Distinct (%)46.7%
Missing59
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean4.1249083
Minimum1.12
Maximum10.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T08:48:39.828746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile2.538
Q13.39
median4
Q34.81
95-th percentile6.016
Maximum10.48
Range9.36
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.1613229
Coefficient of variation (CV)0.28153907
Kurtosis3.3551237
Mean4.1249083
Median Absolute Deviation (MAD)0.7
Skewness1.0133115
Sum2924.56
Variance1.3486708
MonotonicityNot monotonic
2025-11-25T08:48:39.877072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410
 
1.3%
4.119
 
1.2%
3.627
 
0.9%
4.576
 
0.8%
3.896
 
0.8%
3.486
 
0.8%
4.936
 
0.8%
3.686
 
0.8%
2.775
 
0.7%
3.525
 
0.7%
Other values (321)643
83.7%
(Missing)59
 
7.7%
ValueCountFrequency (%)
1.121
0.1%
1.221
0.1%
1.291
0.1%
1.381
0.1%
1.541
0.1%
1.591
0.1%
1.82
0.3%
1.851
0.1%
2.012
0.3%
2.061
0.1%
ValueCountFrequency (%)
10.481
0.1%
10.291
0.1%
9.282
0.3%
9.041
0.1%
8.651
0.1%
7.71
0.1%
7.591
0.1%
7.31
0.1%
7.281
0.1%
6.821
0.1%

hdl_cholesterol_mmol_L_consolidated
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)24.5%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.1211127
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T08:48:39.923367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.51
Q10.83
median1.07
Q31.37
95-th percentile1.8855
Maximum3.7
Range3.42
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.44352229
Coefficient of variation (CV)0.39560902
Kurtosis4.4712255
Mean1.1211127
Median Absolute Deviation (MAD)0.26
Skewness1.2913394
Sum795.99
Variance0.19671202
MonotonicityNot monotonic
2025-11-25T08:48:39.971510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.813
 
1.7%
1.0413
 
1.7%
0.8513
 
1.7%
0.9313
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
0.9511
 
1.4%
110
 
1.3%
0.8410
 
1.3%
0.879
 
1.2%
Other values (164)594
77.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

ldl_cholesterol_mmol_L_consolidated
Real number (ℝ)

High correlation  Missing 

Distinct261
Distinct (%)36.8%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.6717042
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T08:48:40.019243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6745
Q11.11
median1.535
Q32.07
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.77008108
Coefficient of variation (CV)0.4606563
Kurtosis1.8978142
Mean1.6717042
Median Absolute Deviation (MAD)0.475
Skewness1.0866871
Sum1186.91
Variance0.59302488
MonotonicityNot monotonic
2025-11-25T08:48:40.067333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.019
 
1.2%
1.129
 
1.2%
1.329
 
1.2%
1.378
 
1.0%
1.298
 
1.0%
1.187
 
0.9%
2.067
 
0.9%
1.947
 
0.9%
1.267
 
0.9%
1.767
 
0.9%
Other values (251)632
82.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.7%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

fasting_glucose_mmol_L
Real number (ℝ)

Missing 

Distinct276
Distinct (%)37.5%
Missing32
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.928356
Minimum0.95
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T08:48:40.112772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.35
Q14.5
median4.93
Q35.4125
95-th percentile6.12
Maximum15
Range14.05
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.95305831
Coefficient of variation (CV)0.1933826
Kurtosis19.566982
Mean4.928356
Median Absolute Deviation (MAD)0.45
Skewness1.5235001
Sum3627.27
Variance0.90832015
MonotonicityNot monotonic
2025-11-25T08:48:40.161069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.219
 
1.2%
4.759
 
1.2%
5.428
 
1.0%
4.828
 
1.0%
4.938
 
1.0%
4.78
 
1.0%
5.248
 
1.0%
4.737
 
0.9%
4.577
 
0.9%
5.177
 
0.9%
Other values (266)657
85.5%
(Missing)32
 
4.2%
ValueCountFrequency (%)
0.951
0.1%
1.121
0.1%
1.371
0.1%
1.471
0.1%
2.021
0.1%
2.041
0.1%
2.211
0.1%
2.221
0.1%
2.261
0.1%
2.552
0.3%
ValueCountFrequency (%)
151
0.1%
9.911
0.1%
9.671
0.1%
8.241
0.1%
7.971
0.1%
7.621
0.1%
7.611
0.1%
7.421
0.1%
7.271
0.1%
7.061
0.1%

Interactions

2025-11-25T08:48:39.252590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.010217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.351617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.542150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.742482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.954921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.287516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.088214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.384440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.577366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.778273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.992247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.320045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.165198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.414046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.605981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.812108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.024309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.351786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.196488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.444256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.636895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.845299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.058323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.390545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.281496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.477317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.671950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.880237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.094835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.426331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.315896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.509749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.707676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:38.918327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:39.132259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T08:48:40.260862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)fasting_glucose_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidatedtotal_cholesterol_mmol_L
Age (at enrolment)1.0000.2290.1540.0120.1570.155
BMI (kg/m²)0.2291.0000.1160.0200.1060.096
fasting_glucose_mmol_L0.1540.1161.0000.0080.032-0.066
hdl_cholesterol_mmol_L_consolidated0.0120.0200.0081.0000.2690.507
ldl_cholesterol_mmol_L_consolidated0.1570.1060.0320.2691.0000.566
total_cholesterol_mmol_L0.1550.096-0.0660.5070.5661.000

Missing values

2025-11-25T08:48:39.471641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T08:48:39.526762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T08:48:39.581976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Age (at enrolment)SexBMI (kg/m²)study_weektotal_cholesterol_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidatedfasting_glucose_mmol_L
21719.4NaN24.2NaN2.771.231.415.03
21839.4NaN33.6NaN4.930.901.544.55
219NaNNaN33.1NaN5.111.332.204.76
22040.0NaN33.5NaN5.351.612.376.72
22142.0NaN30.1NaN5.891.713.365.68
22239.0NaN22.0NaN3.971.163.035.03
22340.0NaN21.5NaN2.520.522.484.32
22440.0NaN21.2NaN4.170.952.715.48
22541.0NaN21.6NaN4.471.042.605.26
22622.2NaN19.3NaN3.130.902.174.25
Age (at enrolment)SexBMI (kg/m²)study_weektotal_cholesterol_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidatedfasting_glucose_mmol_L
97525.9NaN21.5NaN4.961.141.574.76
976NaNNaN22.6NaN6.241.322.194.97
97727.0NaN23.6NaN6.821.962.68NaN
97833.3NaN32.4NaN3.701.101.055.38
97934.0NaN34.4NaN5.321.401.725.50
98034.0NaN37.3NaN4.111.761.325.99
98135.0NaN37.9NaN2.350.421.356.11
98231.3NaN31.8NaN3.520.911.005.21
98332.0NaN31.2NaN2.931.020.594.67
98432.0NaN33.2NaNNaNNaNNaN5.76